Advances in Explainable Artificial Intelligence (XAI): 3rd Edition

Dear Colleagues,

Recently, artificial intelligence has seen a shift in focus towards the design and deployment of intelligent systems that are interpretable and explainable, with the rise of a new field: explainable artificial intelligence (XAI). This has been echoed both in the research literature and in the press, attracting scholars from all around the world as well as a lay audience. Initially devoted to the design of post hoc methods for explainability, essentially wrapping machine- and deep-learning models with explanations, it is now expanding its boundaries to ante hoc methods for the production of self-interpretable models. Along with this, neuro-symbolic approaches for reasoning have been employed in conjunction with machine learning in order to extend modeling accuracy and precision with self-explainability and justifiability. Scholars have also started shifting the focus towards the structure of explanations since the ultimate users of interactive technologies are humans, linking artificial intelligence and computer sciences to psychology, human–computer interaction, philosophy, and sociology.

It is certain that explainable artificial intelligence is gaining momentum, and this Special Issue calls for contributions exploring this new fascinating area of research, seeking articles that are devoted to the theoretical foundation of XAI, its historical perspectives, and the design of explanations and interactive human-centered intelligent systems with knowledge–representation principles and automated learning capabilities, not only for experts but for the lay audience as well.

Dr. Luca Longo
Guest Editor

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Machine Learning and Knowledge Extraction is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• explainable artificial intelligence (XAI)
• neuro-symbolic reasoning for XAI
• interpretable deep learning
• argument-based models of explanations
• graph neural networks for explainability
• machine learning and knowledge graphs
• human-centric explainable AI
• interpretation of black-box models
• human-understandable machine learning
• counterfactual explanations for machine learning
• natural language processing in XAI
• quantitative/qualitative evaluation metrics for XAI
• ante and post hoc XAI methods
• rule-based systems for XAI
• fuzzy systems and explainability
• human-centered learning and explanations
• model-dependent and model-agnostic explainability
• case-based explanations for AI systems
• interactive machine learning and explanations